Quantifying the Spatiotemporal Dynamics of Engineered Cardiac Microbundles

TL;DR

This paper introduces an open-source computational pipeline for analyzing spatiotemporal contractile dynamics in cardiac microbundles, enabling standardized, reproducible measurements of tissue behavior from microscopy videos.

Contribution

The authors develop and release a scalable, interpretable framework with 16 metrics and core analysis tools for quantifying cardiac tissue dynamics from microscopy data.

Findings

The pipeline reveals continuous variation in contractile phenotypes across samples.

Intra-condition variability often exceeds differences between experimental conditions.

Contraction is mainly driven by a global isotropic mode with localized deformation patterns.

Abstract

Brightfield time-lapse imaging is widely used in cardiac tissue engineering, yet the absence of standardized, interpretable analytical frameworks limits reproducibility and cross-platform comparison. We present an open, scalable computational pipeline for quantifying spatiotemporal contractile dynamics in microscopy videos of human induced pluripotent stem cell-derived cardiac microbundles. Building on our open-source tools "MicroBundleCompute" and "MicroBundlePillarTrack," we define a suite of 16 interpretable structural, functional, and spatiotemporal metrics that capture tissue deformation, synchrony, and heterogeneity. The framework integrates full-field displacement tracking, strain reconstruction, spatial registration, dimensionality reduction, and topology-based vector-field analysis within a unified workflow. Applied to a dataset of 670 cardiac microbundles spanning 20 experimental conditions, the pipeline reveals continuous variation in contractile phenotypes rather than discrete condition-specific clustering, with intra-condition variability often exceeding inter-condition differences. Redundancy analysis identifies a reduced core set of 10 metrics that retain most informational content while minimizing multicollinearity. Analysis of denoised displacement fields shows that contraction is dominated by a global isotropic mode, with localized saddle-type deformation patterns present in approximately half of the samples. All software and workflows are released openly to enable reproducible, scalable analysis of dynamic tissue mechanics.